Dybe bioporers forekomst og betydning for pesticidudvaskning i moræneler
Bibliographic record
Abstract
Dybe bioporers arealmæssige forekomst og betydning for pesticidudvaskningen mellem jordoverfladen og kemisk reduceret grundvandszone i moræneler I projektet blev det påvist, at sprækker i jorden i sig selv ikke er aktive transportveje med mindre, der er bioporer, såsom regnormegange og rødder, i dem. Projektet bekræfter makroporers betydning for transport af pesticider til grundvandet. Projektet viser at forbundne, dybe bioporer er nødvendige for, at der kan ske en hurtig transport af pesticider til grundvandet. Projektet viser igen vigtigheden af opdaterede vejrdata til modellering, da det er ekstrem nedbør, der er bestemmende for udvaskning af pesticider i makroporer. Hyppigheden af ekstremnedbørshændelser er tiltaget som følge af klimaforandringerne og forventes at blive hyppigere i årene fremover, hvorfor gamle vejrdata vil give misvisende resultater, når de bruges i modellerne. Endelig giver projektet konkret viden om, hvordan boringer bør udføres, og hvordan prøver bør udtages fra grundvandet for at sikre en retvisende vurdering af koncentrationen i grundvandet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".